Abstract

Analyzing and categorizing the style of visual art images, especially paintings, is gaining popularity owing to its importance in understanding and appreciating the art. Motivated by this peculiarity, we introduce a novel knowledge distilling strategy to assist visual feature learning in the convolutional neural network for painting style classification. More specifically, a multi-factor distribution is employed as soft-labels to distill complementary information with visual input, which extracts from different historical context via label distribution learning. The proposed method is well-encapsulated in a multi-task learning framework which allows end-to-end training. We demonstrate the superiority of the proposed method over the state-of-the-art approaches on Painting91, OilPainting, and Pandora dataset.